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Cool stuff! Never knew that you could 3D print something that was actually edible, though it makes a lot of sense. I like the idea of using this for improving food quality and health in general. However, I think it could have an immediate impact in developing countries by providing people with a reliable source of food manufactured at a low price.
I think the only way this company can meaningfully differentiate itself from larger, more cash-rich competitors is by understanding the local culture and tastes of the people in the regions that they compete in. They could provide differentiated products for the local markets that an American or Chinese company would have difficulty replicating, given their global focus. I also think the Swiggy brand, given that it is an Indian-born company, is a huge asset and source of pride for its citizens that could be leveraged.
This is a great example of a situation where 3D printing can provide value for people who don’t necessarily care too much about quality. I would imagine this organization would have to partner with many villages to understand typical use cases of the product and potentially have one of these set up with an easily usable UI in a central part of the village. I don’t think this has to be used only in emergencies and imagine it could provide value in daily life as well. Villagers, though many likely don’t have mobile phones, would be able to quickly adopt the technology, given that the product is designed with the consumer in mind.
Really interesting post, Ankur! It’s nice to see that Amazon tries these types of approaches across their many business lines. I see some similarities with this approach and how they allow people to self-publish books on their Kindle service. Even if these tactics don’t bring in significant revenues, it does get users and customers engaged on their platform. It also potentially creates future partners on new work at Amazon Studios. Nice.
Really cool to understand how machine learning can be applied to an industry as traditional as shipping and transportation! I agree with you that the team should focus on building out their machine learning capabilities, although it is interesting to think about them leveraging the “AI-as-a-service” model. With the knowledge that IBM (and potentially other tech companies) are entering the space, my main fear that is they would be able to quickly utilize their own machine learning capabilities to replicate Flexport’s product and provide better insights to their end users. I imagine that developing ML capabilities in-house would allow them to (1) quickly identify insights that could serve their end users, (2) improve their ability to make the software as pain-free as possible for the data entry process, and (3) raise more funding to compete with the larger tech companies, given that ML tends to attract investors. I don’t think this necessarily requires them to choose between operations and machine learning. They could use their machine learning capabilities to improve their operations, attract talent, and potentially expand their capabilities for their customers.